Name
Abstract | ||
|---|---|---|
We present a novel method for automatic fingerspelling recognition which is able to discriminate complex hand configurations with high amounts of finger occlusions. Such a scenario, while common in most fingerspelling alphabets, presents a challenge for vision methods due to the low intensity variation along important shape edges in the hand image. Our approach is based on a simple and cheap modification of the capture setup: a multi-flash camera is used with flashes strategically positioned to cast shadows along depth discontinuities in the scene, allowing efficient and accurate hand shape extraction. We then use a shift and scale invariant shape descriptor for fingerspelling recognition, demonstrating great improvement over methods that rely on features acquired by traditional edge detection and segmentation algorithms. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/CVPR.2004.336 | CVPR Workshops |
Field | DocType | Volume |
Computer vision,Classification of discontinuities,Scale invariance,Pattern recognition,Edge detection,Computer science,Segmentation,Vision based,Artificial intelligence,Fingerspelling,Hidden Markov model,Vocabulary | Conference | 2004 |
Issue | ISBN | Citations |
1 | 0-7695-2158-4 | 23 |
PageRank | References | Authors |
1.27 | 15 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rogério Feris | 1 | 1529 | 89.95 |
| Matthew Turk | 2 | 3724 | 499.42 |
| Ramesh Raskar | 3 | 5305 | 422.69 |
| Kar-han Tan | 4 | 622 | 43.39 |
| Gosuke Ohashi | 5 | 39 | 7.32 |